Research on Relation Extraction Method Based on Associative Adjacency Matrix

doi:10.19678/j.issn.1000-3428.0069586

Abstract

Abstract:

Graph neural network can effectively aggregate information from several nodes and encode structural information of sentences, thus being widely applied in relation extraction tasks. However, current relation extraction methods based on graph neural network often require the assistance of external parsing tools to construct dependency trees, a process that may introduce errors leading to incorrect information propagation. To address this issue, this paper proposes a Graph Convolutional Neural Network (GCN) model based on an association adjacency matrix for relation extraction. This model first utilizes the Robustly optimized BERT approach (RoBERTa) Pre-trained Language Model (PLM) to convert each word into vector representations and calculates the association between word vectors using their dot product. Subsequently, based on the association between words and relative entity position features, it constructs an association adjacency matrix and utilizes GCN to extract semantic structural features of sentences. Finally, it mitigates the gradient vanishing problem during model training using residual connections and obtains the final classification representation by fusing sentence and entity representations. This model avoids error propagation caused by the use of external parsing tools. Experimental results demonstrate that compared to existing graph convolution-based models, the proposed model achieves good performance in relation extraction tasks in Temporal Action and Relation Corpus (TACRED) and Re-TACRED datasets, with precision, recall, and F1 value of 68.8%, 77.5%, 72.8% and 90.5%, 91.3%, 90.9%, respectively, validating the effectiveness and feasibility of the model.

Key words: relation extraction, positional information, associative adjacency matrix, graph neural network, structural information

摘要：

图神经网络能够有效地聚合节点间的信息、编码句子的结构信息, 因此被广泛应用于关系抽取任务。然而, 目前基于图神经网络的关系抽取方法常需要借助外部解析工具构建依赖树, 这一过程可能会产生误差, 导致错误的信息传递。为了解决上述问题, 提出一种基于关联邻接矩阵的图卷积神经网络(GCN)模型用于关系抽取。首先, 通过RoBERTa(Robustly optimized BERT approach)预训练语言模型(PLM)将每个词转换为向量表示, 并通过点乘计算词向量之间的关联度。然后, 基于词之间的关联度和相对实体位置特征构建关联邻接矩阵, 并利用GCN提取句子的语义结构特征。最后, 利用残差连接缓解模型训练过程中的梯度消失问题, 并通过融合句子表示和实体表示得到最终的分类表示。该模型避免了使用外部解析工具可能引起的误差传播。实验结果表明, 与现有基于图卷积的模型相比, 其在TACRED(Temporal Action and Relation Corpus)和Re-TACRED数据集的关系抽取任务上精确率、召回率、F1值分别获得了68.8%、77.5%、72.8%和90.5%、91.3%、90.9%的良好性能, 验证了该模型的有效性和可行性。

关键词: 关系抽取, 位置信息, 关联邻接矩阵, 图神经网络, 结构信息

YANG Run, CHEN Yanping, YAN Jiaxin, QIN Yongbin. Research on Relation Extraction Method Based on Associative Adjacency Matrix[J]. Computer Engineering, 2025, 51(10): 121-129.

杨润, 陈艳平, 闫家鑫, 秦永彬. 基于关联邻接矩阵的关系抽取方法研究[J]. 计算机工程, 2025, 51(10): 121-129.

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References 39

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